static void Main(string[] args)
{
// this form of ANN uses genetic algorithm to produce
// hidden layer of neurons
// A NEAT network starts with only an
// input layer and output layer. The rest is evolved as the training progresses.
// Connections inside of a NEAT neural network can be feedforward, recurrent,
// or self - connected.All of these connection types will be tried by NEAT as it
// attempts to evolve a neural network capable of the given task.
IMLDataSet trainingSet = new BasicMLDataSet(XORInput, XORIdeal);
NEATPopulation pop = new NEATPopulation(2, 1, 1000);
pop.Reset();
pop.InitialConnectionDensity = 1.0; // not required, but speeds processing.
ICalculateScore score = new TrainingSetScore(trainingSet);
// train the neural network
TrainEA train = NEATUtil.ConstructNEATTrainer(pop, score);
EncogUtility.TrainToError(train, 0.01);
NEATNetwork network = (NEATNetwork)train.CODEC.Decode(train.BestGenome);
// TODO no persistance? no means to peek structure?
// test the neural network
Console.WriteLine(@"Neural Network Results:");
EncogUtility.Evaluate(network, trainingSet);
}
/// <summary>
/// Create an NEAT GA trainer.
/// </summary>
/// <param name="method">The method to use.</param>
/// <param name="training">The training data to use.</param>
/// <param name="argsStr">The arguments to use.</param>
/// <returns>The newly created trainer.</returns>
public IMLTrain Create(IMLMethod method,
IMLDataSet training, String argsStr)
{
ICalculateScore score = new TrainingSetScore(training);
TrainEA train = NEATUtil.ConstructNEATTrainer((NEATPopulation)method, score);
return(train);
}
private void Validate(NEATPopulation pop)
{
Assert.AreEqual(10, pop.PopulationSize);
Assert.AreEqual(0.2, pop.SurvivalRate);
// see if the population can actually be used to train
IMLDataSet trainingSet = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
ICalculateScore score = new TrainingSetScore(trainingSet);
IEvolutionaryAlgorithm train = NEATUtil.ConstructNEATTrainer(pop, score);
train.Iteration();
}
public void ResetTraining()
{
Substrate substrate = SubstrateFactory.factorSandwichSubstrate(11, 11);
BoxesScore score = new BoxesScore(11);
pop = new NEATPopulation(substrate, 500);
pop.ActivationCycles = 4;
pop.Reset();
train = NEATUtil.ConstructNEATTrainer(pop, score);
OriginalNEATSpeciation speciation = new OriginalNEATSpeciation();
train.Speciation = new OriginalNEATSpeciation();
}
private NEATPopulation Generate()
{
IMLDataSet trainingSet = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
ICalculateScore score = new TrainingSetScore(trainingSet);
// train the neural network
ActivationStep step = new ActivationStep();
step.Center = 0.5;
IEvolutionaryAlgorithm train = NEATUtil.ConstructNEATTrainer(
score, 2, 1, 10);
return((NEATPopulation)train.Population);
}
/// <summary>
/// The entry point for this example. If you would like to make this example
/// stand alone, then add to its own project and rename to Main.
/// </summary>
/// <param name="args">Not used.</param>
public static void ExampleMain(string[] args)
{
IMLDataSet trainingSet = new BasicMLDataSet(XORInput, XORIdeal);
var pop = new NEATPopulation(2, 1, 1000);
pop.Reset();
pop.InitialConnectionDensity = 1.0; // not required, but speeds processing.
ICalculateScore score = new TrainingSetScore(trainingSet);
// train the neural network
var train = NEATUtil.ConstructNEATTrainer(pop, score);
EncogUtility.TrainToError(train, 0.01);
var network = (NEATNetwork)train.CODEC.Decode(train.BestGenome);
// test the neural network
Console.WriteLine(@"Neural Network Results:");
EncogUtility.Evaluate(network, trainingSet);
}
